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    Analysis disease progression using data visualization

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    Authors
    Liu, Enjie
    Zhao, Youbing
    Wei, Hui
    Kaldoudi, Eleni
    Roumeliotis, Stefanos
    Affiliation
    University of Bedfordshire
    University of Thrace
    Issue Date
    2018-02-01
    Subjects
    Internet of Things
    visual analytics
    risk factors
    cata visulisation
    
    Metadata
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    Abstract
    Patients with chronic diseases are required to self-manage their conditions. Patients are normally advised to adapt to healthier life-style, and in the meantime to continuously monitor the relevant biomarkers. Recent technology advances in monitoring devices, such as activities waist bands and glucose sensors, made it much easier for the patients to monitor the level of activities and biomarkers in home environment. The aim is to assist patients in making informed decisions and the key feature to achieve will be based on thoroughly understand the meaning of the collected data with the help of known facts (knowledge). However, interpreting the meaning of the monitored data is a challenging task for an ordinary patient. Data visualization techniques play an important role in helping users to understand and interpret data via exploration. In this paper, we present data visualization diagrams that are used in CARRE project to help both medical professional and patients to understand the disease progressions.
    Citation
    Liu E, Zhao Y, Wei H, Kaldoudi E, Roumeliotis S (2018) 'Analysis disease progression using data visualization', 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Exeter, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/623860
    DOI
    10.1109/iThings-GreenCom-CPSCom-SmartData.2017.135
    Additional Links
    https://ieeexplore.ieee.org/document/8276854
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781538630655
    ae974a485f413a2113503eed53cd6c53
    10.1109/iThings-GreenCom-CPSCom-SmartData.2017.135
    Scopus Count
    Collections
    Computing

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